System and method for identifying and classifying color regions from a digital image

ABSTRACT

A system and method of identifying and classifying regions of a digital image. The method includes an initial step of inputting an image as a color digital image. Subsequently, information that identifies color regions of the color digital image is obtained. Finally, color and non-color regions of the color digital image are classified based upon the identifying information.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application Ser.No. 60/911,595 filed on Apr. 13, 2007, which is hereby incorporated byreference in its entirety.

FIELD OF THE INVENTION

The present invention relates generally to a system and method foridentifying and classifying color regions from a digital image and moreparticularly to a system and method that automatically converts adigitized color document to black and white when no useful colorelements are detected.

BACKGROUND OF THE INVENTION

Many documents contain black and white textual content as well asvaluable color information, such as photos, logos, stamps and othercolor markings. Examples of such documents include invoices, medicalrecords, insurance claims and the like. The traditional process involvedin digitizing these documents is to scan them as black and white. Whilethis process creates a highly compressed image and small file size, allcolor information is lost.

Alternatively, such documents are scanned as color resulting in asubstantially larger file than the equivalent black and white versionbut retaining all color information. As will be appreciated, whendocuments contain just black and white or gray textual elements, theadditional color information is not useful. In known systems that scanas color, a manual review process must be used to convert thesedigitized color images to black and white for storage and bandwidthoptimizations.

With the foregoing problems and concerns in mind, it is the generalobject of the present invention to provide a system and method foridentifying and classifying color regions. In particular, the presentinvention provides a system that automatically converts a digitizedcolor document to black and white when no useful color elements aredetected.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide a system and methodfor identifying and classifying color regions from a digital image.

It is another object of the present invention to provide a system andmethod for identifying and classifying color regions from a digitalimage to optimize storage and bandwidth.

It is another object of the present invention to provide a system andmethod for detecting whether a digital image contains useful colorelements.

It is another object of the present invention to provide a system andmethod for automatically converting a digitized color document to blackand white when no useful color elements are found.

It is another object of the present invention to provide a system andmethod for generating an optimized, highly compressed multi-layer filecontaining a digitized image.

It is another object of the present invention to provide a system andmethod that facilitates automated document sorting, region of interestclassification, targeting and other tasks requiring image featuredifferentiation.

It is another object of the present invention to provide a system andmethod for digitizing an image that ignores color noise from a scanner.

It is another object of the present invention to provide a system andmethod for digitizing an image that allows for fast documentdigitization and the identification of documents that are in need ofmanual review.

It is yet another object of the present invention to provide a systemand method for digitizing an image in which information and data from aprior step can be utilized in subsequent steps to expedite and reducecosts associated with identifying and classifying regions from a digitalimage.

An embodiment of the present invention is a system and method ofidentifying and classifying regions of a digital image. The methodincludes an initial step of inputting an image as a color digital image.Subsequently, information that identifies color regions of the colordigital image is obtained. Finally, color and non-color regions of thecolor digital image are classified based upon the identifyinginformation.

These and other objectives of the present invention, and their preferredembodiments, shall become clear by consideration of the specification,claims and drawings taken as a whole.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a system for identifying andclassifying color regions in accordance with an embodiment of thepresent invention.

FIG. 2 is a flowchart of a method of identifying and classifying colorregions in accordance with an embodiment of the present invention.

FIG. 3 is a simplified flowchart of input arguments according to themethod of FIG. 2.

FIG. 4 is a graphical illustration of how the system and method of FIGS.1 and 2 segments and detects and segments color from black and white forseparate compression.

FIG. 5 is a graphical illustration of how the system and method of FIGS.1 and 2 segments and combines multiple layers from a digitized image.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Referring generally to FIGS. 1-6, an embodiment of the present inventionrelates to a method or system for analyzing a digital image. Inparticular, the present invention relates to analyzing and classifying adigital image that has been scanned and digitized as a color image. Aswill be appreciated, when such images are digitized, each of theresulting pixels has associated color information, such as hue,saturation and value (“HSV”), or hue, saturation and lightness (“HSL”).When images are scanned as color, however, they create a relativelylarge file compared to images scanned as black and white. In an effortto reduce file sizes for storage and bandwidth optimization, it isdesirable to convert color digital images that do not contain any usefulcolor information to black and white. In known systems, a manual reviewprocess must be employed to accomplish this conversion.

In contrast, the present invention provides a system and method ofclassifying a color digital image into multiple categories. Thesecategories or elements include backgrounds, color photos, gray photos,and color markings. This information can be used to automaticallyconvert a digitized color document to black and white when no usefulcolor elements are detected. Moreover, the inventive system and methodignores color noise often introduced from scanner charge coupled devicesand generates information can be used to generate an optimized, highlycompressed multi-layer file format such as an Adobe PDF®, MicrosoftXPS®, or other proprietary multi-layer format.

The resulting information can be used for automated document sorting,region of interest classification, targeting and other tasks requiringimage feature differentiation.

Referring to FIG. 1, the inventive method is preferably executed bysoftware residing within a processor-equipped device 10. Theprocessor-equipped device 10 is operatively connected to a color imagecapture device 20 such as a scanner. The processor-equipped device 10 isalso operatively connected to a storage or memory device 30 for storingimage files. Preferably, the file

Turning now to FIG. 2, a preferred method of the present invention isillustrated. As an initial step 100, the present system evaluates “bitdepth,” e.g., the number of bits used to store information about eachpixel of an image. The higher the depth, the more colors that areavailable for storage. The bit depth of an image will determine how manylevels of gray (or color) can be generated. In the present system, thebit depth is assessed and if the depth corresponds to a black and whiteimage or a gray scale image, then the color extraction process isterminated resulting in an output result of an empty set of colorregions 210.

In FIG. 2, this decision process has been labeled with ‘Yes’ and ‘No.’If the bit depth is that of a black and white or gray scale image, e.g.,‘Yes,’ then the output result is an empty color region 210. If the bitdepth is not that of a black and white or gray scale image, e.g., ‘No,’then the inventive process continues.

For example, black and white images may have a bit depth of 1. If theinventive system detects a bit depth of 1, the color extraction processwill be terminated. If, however, a color bit depth is detected, say forexample a depth of 8-bits or 24-bits, the next step in the colorextraction process, resampling the image is initiated. As stated, a usercan preferably select or modify the criteria used for the bit depthanalysis as one of the input arguments.

In the resampling step, step 110, the image is resampled and rescaleddown to a smaller size to expedite analysis of the document. Preferably,a user can select the extent to which an image is rescaled, e.g., by ½,¼ or ⅛ of the original size. Once the image has been resized, blackborders are detected and cropped at step 120.

In this process, the image is scanned to determine if it is a blackborder by examining the color vector of each pixel. In assessing blackborders, a determination is made that if a pixel exceeds a predeterminedvalue it is black, if not, it is considered ‘noise.’ When the totalamount of noise has exceeded 1% of the dimension being scanned, it isthen considered ‘signal’ and is considered a non-black border. As willbe appreciated, the black borders are cropped and non-black borderportions, i.e., those assessed as ‘signal,’ are retained. If all pixelsin an image are black border elements, then the color extraction processis terminated with the output result of an empty set of color regions.This decision process is noted with Q1, which refers to thedetermination of whether the entire document is black. Upon detectionand cropping of border elements, a gray element analysis instituted, asnoted at step 130.

In the gray element analysis, a function ColorToGray(x, y, z) is used tomap from an RGB color vector space to a gray scale vector space. A grayscale image is created from the input color image using ColorToGrayfunction. This RGB color to gray transformation may be accomplished withknown, conventional processes/algorithms. Once the transformation hastaken place, for each pixel of a picture element of which value isrepresented by a RGB vector (x, y, z), the distance to gray scale spaceis evaluated between from (x, y, z) to (v, v, v) where v=ColorToGray(x,y, z). Preferably, a Malahanobis distance statistical methodology isemployed. In this manner, a determination is made as to whether theoriginal scanned document is gray scale by comparing the original to thegray scale transformation. A pixel is considered a gray element if thedistance is less than a user specified threshold value. As noted at Q2,if a percentage of gray elements is greater than an internally specifiedthreshold value, then an image is determined to be completely gray andcontains no useful color information. The color extraction process isthen terminated with the output result of an empty set of color regions.If, however, the picture element is not all gray, then a backgroundsegmentation process, step 140, is initiated.

In the background segmentation process, every element that is not text,pictures or images is deemed background including margin space and thelike. Initially, the process involves the creation of a new memoryspace. This memory space has a size of w×h, where w=width of image andh=height of image. An HSL color transformation is then applied to theinput color digital image. For each pixel of the input image, an Hcomponent, i.e., hue, of the HSL vector will be copied into the memoryspace just created. An unsupervised clustering method is then applied tothe H component data to identify background colors if any such colorsare present. If the set of background colors is not empty, then theregions of background are identified as follows.

First, an image slicing method is applied to the H component data toobtain a binary image in which a background color is represented byblack pixels and all other colors present are white. Known slicingmethodologies may be employed. Portions of the image that can be slicedare background, those that could not be sliced are non-backgroundportions. After slicing, a set of connected components of black pixels,i.e., background color, on the binary image is identified. A sizeanalysis method is then applied to the bounding boxes of the connectedcomponents to determine whether certain bounding boxes are to be mergedtogether or discarded. Only merged bounding boxes are considered as aset of background regions. Each pixel in identified background regionsis considered to be a gray element. If a percentage of occupancy of grayelements is greater than an internally specified threshold value, e.g.,the document/image is entirely background such as, for example, a blanksheet of colored paper stock, then the color extraction process will beterminated resulting in an empty set of color regions.

The background segmentation step is an important aspect of the presentinvention as it effectively segments background from non-backgroundelements such as images. In particular, the sub-steps of first applyingHSL, then slicing and then merging bounding boxes is believed to beunknown in the art and provides an excellent method of identifying andsegmenting background elements.

Once the background elements have been segmented, an optional photoidentification step, step 150, is initiated. As will be appreciated, auser may opt to skip this step should the documents/images include nophotographic elements. The selection process for step 150 is depicted atQ4. Photo identification is accomplished through the application ofmedian filtration methods to the gray-scaled image created at step 130,i.e., the gray element analysis. Two different median filtering methodsare applied. First, filtering is done with a large window size and thenfiltering with a short window size. A difference image between the twoimages is created from the two filtering methods and then converted to abinary image using an adaptive threshold method. A connected componentsmethod is then applied to the binary image to obtain a set of boundingboxes of black pixels. A size analysis method is then applied to the setof connected components in which bounding boxes having regions of smallsize are rejected as being non-photo regions. The photo region is theremaining set of bounding boxes of the connected components.

Once any photo regions have been identified or the photo identificationstep has been skipped, color regions are subsequently identified at step160. As will be appreciated, a user may opt to skip this step as welland the selection process is noted at Q5. In step 160, the originalcolor image is converted to a binary image. Those pixels marked as grayelements or in any photo regions are set as white pixels while the restare set as black pixels. A connected components method is applied toobtain the bounding boxes of connected components of black pixels. Theoverlapped bounding boxes are merged into a single large bounding box. Asize analysis method is then applied on the set of bounding boxes inwhich connected components having bounding box of smaller size than auser specified threshold value are rejected. Such smaller boxes could bethose created by color noise, for example. The remaining set of boundingboxes of the connected components is regarded as the color region.

Identifying color regions is another important aspect of the presentinvention. Many documents contain black and white textual content aswell as valuable color information. These documents are typicallyscanned as color resulting in a substantially larger file than anequivalent black and white version but retaining all color information.As will be appreciated, when documents contain just black and white orgray textual elements, the additional color information is not useful.In known systems that scan as color, a manual review process must beused to convert these digitized color images to black and white forstorage, bandwidth and readability optimizations. The present inventionprovides a method and system in which the manual detection andseparation of color elements is unnecessary.

The next step in the inventive process, step 170, is to grow and mergephoto and color regions. Each box of photo regions obtained from thephoto identification step is expanded up to an internally specifiedvalue to find overlapping photo regions. If any overlapping regions arefound, they are merged into a single photo region. Likewise, each box ofany color regions obtained from the color identification step isexpanded up to an internally specified value to find any overlappingcolor regions. If overlapping color regions are found, they are mergedinto a single color region.

Finally, the image/document is restored and resized at step 180 usingthe rates of resizing applied at the resampling step 120. The scale isrestored to the regions of color marks and photos. The color extractionprocess returns the resulting photo and color regions.

In this process, photo and color regions are separated fromnon-photo/color regions. This is graphically illustrated at boxes 190and 200 in FIG. 2. The empty region discussed above is graphicallydepicted at reference number 210.

As mentioned above, there are several steps in the inventive method andsystem in which a user can enter input arguments. These input arguments210 are graphically depicted in FIG. 3 and include a pointer to theimage data; a triplet of integer values of width, height and row strideof the image; two Boolean options “DetectColorMarkings” and“DetectPhotos”; an integer value of saturation threshold; an integervalue of threshold for distance to gray scale space; an integer value ofvalid interval for size of color regions; an integer value of validinterval for size of photo regions; and an integer value of the resizingrates.

The output from the inventive system is that photo regions and colorregions are represented by sets of vectors for pixel coordinate in theinput image.

Referring now to FIGS. 4 and 5 the present invention provides aconvenient method of detecting color and separating color from black andwhite text in documents scanned as color. This is significant in that itallows color images 300 and the like to be compressed differently fromblack and white text 310. For example, color images/photos may becompressed with JPEG, which is appropriate for such images. The JPEGfiles will be relatively large, however. Black and white text, however,should optimally not receive this same compression as the resulting filewill be unnecessarily large and JPEG is a lossy compression and not wellsuited for text. As such, text may be compressed with JBIG2/CCIT G4, abilevel format appropriate for text. In this way, both file size andimage readability can be optimized.

Moreover, once the different elements of an image have been separatedand individually compressed, they may be recombined. In particular, themultiple compressed layers such as JBIC2/CCIT G4, JPEG and Flate can becombined in a PDF file 320. As will be readily appreciated, this PDFfile will have a substantially smaller size then if the initial documentwith both color and black and white text were saved as a PDF.

An additional important aspect of the present invention is that theapplication of many of the inventive steps functions as a filterreducing the amount of information to process in subsequent steps. Forexample, applying a median filter to an entire image with a large windowis an expensive operation, but having already sliced the image, theamount of data to be filtered is reduced. Likewise, detecting backgroundcolor first and only building and analyzing connected components fromthe detected color is potentially less expensive than doing that acrossan entire image.

While the invention has been described with reference to the preferredembodiments, it will be understood by those skilled in the art thatvarious obvious changes may be made, and equivalents may be substitutedfor elements thereof, without departing from the essential scope of thepresent invention. Therefore, it is intended that the invention not belimited to the particular embodiments disclosed, but that the inventionincludes all equivalent embodiments.

1. A method of identifying and classifying regions of a digital image,said method comprising the steps of: inputting an image as a colordigital image; obtaining information that identifies color and photoregions of said color digital image; classifying color, photo, non-colorand non-photo regions of said color digital image based upon saididentifying information; and individually compressing said color,non-color, photo and non-photo regions based on a quality characteristicof each of said regions; wherein said photo regions are classified bythe following steps: transforming said color digital image to grayscale; applying a first median filtering method to said gray scale imagecreating a first filtered image; applying a second median filteringmethod to said gray scale image creating a second filtered image;creating a difference image from between said first and second filteredimages; converting said difference image to a binary image; obtaining aset of bounding boxes of black pixels in said binary image through aconnected components method; and applying a size analysis to saidconnected components wherein connected components having a size below apredetermined threshold are marked as non-photo regions and remainingbounding boxes of said connected components are photo regions.
 2. Amethod of identifying and classifying regions of a digital image, saidmethod comprising the steps of: inputting an image as a color digitalimage; obtaining information that identifies color and photo regions ofsaid color digital image; classifying color, photo, non-color andnon-photo regions of said color digital image based upon saididentifying information; individually compressing said color, non-color,photo and non-photo regions based on a quality characteristic of each ofsaid regions; assessing a bit-depth of said color digital image todetermine whether said image is entirely black and white or entirelygrayscale; and terminating said classification of said regions if saidimage has a bit-depth indicating that it is black and white orgrayscale.
 3. A method of identifying and classifying regions of adigital image, said method comprising the steps of: inputting an imageas a color digital image; obtaining information that identifies colorand photo regions of said color digital image; classifying color, photo,non-color and non-photo regions of said color digital image based uponsaid identifying information; individually compressing said color,non-color, photo and non-photo regions based on a quality characteristicof each of said regions; and resampling and resizing said image toexpedite the identification and classification of said regions of saiddigital image.
 4. A method of identifying and classifying regions of adigital image, said method comprising the steps of: inputting an imageas a color digital image; obtaining information that identifies colorand photo regions of said color digital image; classifying color, photo,non-color and non-photo regions of said color digital image based uponsaid identifying information; individually compressing said color,non-color, photo and non-photo regions based on a quality characteristicof each of said regions; identifying black border regions in said image;and removing any identified black border regions.
 5. The method of claim4 wherein said identification of black border elements includes thesteps of: determining whether pixels that comprise said color digitalimage are black; scanning said black pixels along a dimension of saidimage to determine a boundary of said black border; establishing saidboundary of said black border where said non-black pixels exceedone-percent of a total of said pixels of said scanned dimension.
 6. Amethod of identifying and classifying regions of a digital image, saidmethod comprising the steps of: inputting an image as a color digitalimage; obtaining information that identifies color and photo regions ofsaid color digital image; classifying color, photo, non-color andnon-photo regions of said color digital image based upon saididentifying information; individually compressing said color, non-color,photo and non-photo regions based on a quality characteristic of each ofsaid regions; identifying gray elements in said image; and removing anyidentified gray elements.
 7. The method of claim 6 wherein identifyinggray elements includes: transforming said color digital image to grayscale to create a gray scale image; computing a Malahanobis distance foreach color pixel in said color digital image to a corresponding pixel ofsaid transformed gray scale image; and marking a pixel in said colordigital image as gray if it exceeds a predetermined distance from itscounterpart in said gray scale image.
 8. The method of claim 6 whereinsaid identification of color elements includes the following: creating abinary image from said color digital image; marking any pixelspreviously identified as gray or photo elements as white and anyremaining pixels as black; applying a connected components method toobtain bounding boxes of connected components of said black pixels;merging any overlapping bounding boxes into a single bounding box;applying a size analysis method to said bounding boxes in whichconnected components having a bounding box of a smaller size than apredetermined value are marked as non-color and a remaining set ofbounding boxes of said connected components are marked as a colorregion.
 9. A method of identifying and classifying regions of a digitalimage, said method comprising the steps of: inputting an image as acolor digital image; obtaining information that identifies color andphoto regions of said color digital image; classifying color, photo,non-color and non-photo regions of said color digital image based uponsaid identifying information; individually compressing said color,non-color, photo and non-photo regions based on a quality characteristicof each of said regions; identifying background elements; and removingsaid background elements.
 10. The method of claim 9 wherein identifyingbackground elements includes: creating a memory space having a width andheight corresponding to that of said color digital image; applying anHSL color transformation to said color digital image; copying an Hcomponent from said HSL transformation for each pixel of said colordigital image into said memory space; applying an unsupervisedclustering method to said H component data to identify background colorsif present; applying an image slicing method to said H component data toobtain a binary image in which background pixels are black; obtaining aset of bounding boxes of using a connected components methodology forsaid black pixels; and applying a size analysis to determine whichbounding boxes are to be merged and considered background.
 11. A methodof identifying and classifying regions of a digital image, said methodcomprising the steps of: inputting an image as a color digital image;obtaining information that identifies color and photo regions of saidcolor digital image; classifying color, photo, non-color and non-photoregions of said color digital image based upon said identifyinginformation; individually compressing said color, non-color, photo andnon-photo regions based on a quality characteristic of each of saidregions; and merging said photo and said color regions.
 12. The methodof claim 3 including the step of: restoring said image to its originalsize prior to said resampling.
 13. A digital imaging system comprising:a color image capturing device configured for obtaining a digital image;and a processor electrically connected to at least one of the imagecapturing device configured for obtaining information that identifiescolor and photo regions of said color digital image and classifyingcolor, photo, non-photo and non-color regions of said color digitalimage based upon said identifying information, for individuallycompressing said color, non-color, photo and non-photo regions based ona quality characteristic of each of said regions, and for assessing abit-depth of said color digital image to determine whether said image isentirely black and white or entirely grayscale and terminating saidclassification of said regions if said image has a bit-depth indicatingthat it is black and white or grayscale.